Species Distributions
Sarah Zirkle
October 26, 2021
1 Campus Map
campus.kml <- readOGR("C:/Users/sarah/Desktop/biol 5660/gentry half/distributions/distributions/Data/Campus_Points.kml")## OGR data source with driver: KML
## Source: "C:\Users\sarah\Desktop\biol 5660\gentry half\distributions\distributions\Data\Campus_Points.kml", layer: "Campus"
## with 23 features
## It has 2 fieldscampus_points <- cbind(campus.kml@data,campus.kml@coords)
campus_points[2] <- NULL
campus_points[4] <- NULL
colnames(campus_points) <- c("name","x","y")
CampusMap <- openmap(c(36.5360,-87.3570),c(36.5300,-87.3495), type='bing')
APSU <- openproj(CampusMap, projection = "+proj=longlat +ellps=WGS84 +units=m +no_defs")
autoplot.OpenStreetMap(APSU) +
geom_point(data=campus_points, aes(x = x, y = y, color = name), size = 4, alpha = 0.8) +
geom_text(data=campus_points,aes(x = x, y = y, label = name), color="black", vjust=-0.60, size=4.01, fontface="bold") +
geom_text(data=campus_points,aes(x = x, y = y, label = name), color="white", vjust=-0.75, fontface="bold") +
labs(x="Longtiude", y="Latitude") + theme(legend.position = "none")
campolOGR <- readOGR("C:/Users/sarah/Desktop/biol 5660/gentry half/distributions/distributions/Data/campus_poly.kml")## OGR data source with driver: KML
## Source: "C:\Users\sarah\Desktop\biol 5660\gentry half\distributions\distributions\Data\campus_poly.kml", layer: "campus_poly.kml"
## with 1 features
## It has 2 fieldscamp_poly <- as.data.frame(cbind(campolOGR@polygons[[1]]@Polygons[[1]]@coords[,1], campolOGR@polygons[[1]]@Polygons[[1]]@coords[,2]))
colnames(camp_poly) <- c("X","Y")1.1 Add Polygon
autoplot.OpenStreetMap(APSU) +
geom_polygon(data= camp_poly, aes(x=X, y=Y), alpha = .5, size = 2, color="white") +
geom_point(data=campus_points, aes(x = x, y = y, color = name), size = 4, alpha = 0.8) +
geom_text(data=campus_points,aes(x = x, y = y, label = name), color="black", vjust=-0.60, size=4.01, fontface="bold") +
geom_text(data=campus_points,aes(x = x, y = y, label = name), color="white", vjust=-0.75, fontface="bold") +
labs(x="Longtiude", y="Latitude") + theme(legend.position = "none")
2 Species Distribution
| Tri-colored bat | Florida Bonneted Bat |
|---|---|
 |
 |
pesu_dismo <- gbif("perimyotis", species = "subflavus", ext = c(-130,-60,20,60),
geo = TRUE, sp = TRUE, download = TRUE,
removeZeros = TRUE)pesu_dismo_df <- cbind.data.frame(pesu_dismo@coords[,1],
pesu_dismo@coords[,2])
colnames(pesu_dismo_df) <- c("x","y")
us <- map_data("state")
ggplot(data = pesu_dismo_df, aes(x=x, y=y)) +
geom_polygon(data = us, aes(x=long, y = lat, group = group),
fill = "white", color="black") +
geom_point() + xlab("Longitude") + ylab("Latitude") +
coord_fixed(xlim = c(-95,-75), ylim = c(25,41)) +
xlab("Longitude") + ylab("Latitude") + ggtitle("Perimyotis subflavus captures in the Eastern US") +
theme_bw() + theme(plot.title = element_text(hjust = 0.5)) +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "lightblue"))
pesu_rgbif <- occ_data(scientificName = "Perimyotis subflavus",
hasCoordinate = TRUE, limit = 2000,
decimalLongitude = "-95, -75",
decimalLatitude = "24, 41")
bon_rgbif <- occ_data(scientificName = "Eumops floridanus",
hasCoordinate = TRUE, limit = 2000,
decimalLongitude = "-85, -80",
decimalLatitude = "24, 30")pesu_rgbif_df <- cbind.data.frame(pesu_rgbif$data$species,
pesu_rgbif$data$decimalLatitude,
pesu_rgbif$data$decimalLongitude,
pesu_rgbif$data$stateProvince,
pesu_rgbif$data$verbatimLocality)
bon_rgbif_df <- cbind.data.frame(bon_rgbif$data$species,
bon_rgbif$data$decimalLatitude,
bon_rgbif$data$decimalLongitude,
bon_rgbif$data$stateProvince,
bon_rgbif$data$verbatimLocality)
colnames(pesu_rgbif_df) <- c("species","y","x","state","location")
colnames(bon_rgbif_df) <- c("species","y","x","state","location")
pesu_rgbif_df <- pesu_rgbif_df[complete.cases(pesu_rgbif_df[1:4]),]
bon_rgbif_df <- bon_rgbif_df[complete.cases(bon_rgbif_df[1:4]),]ggplot() +
geom_polygon(data = us, aes(x=long, y = lat, group = group),
fill = "white", color="black") +
geom_point(data = pesu_rgbif_df, aes(x=x, y=y, color = species), size = 3) +
geom_point(data = bon_rgbif_df, aes(x=x, y=y, color = species), size = 3) +
coord_fixed(xlim = c(-95,-75), ylim = c(25,41)) +
xlab("Longitude") + ylab("Latitude") + ggtitle("Bat captures in the Eastern US") +
guides(color=guide_legend("Legend", override.aes = list(size = 4))) +
theme_bw() + theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position = "bottom") +
theme(legend.title.align = 0.5, legend.box.just = "center") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "lightblue"))
colors <- colorFactor(c("#F8766D","#00BA38","#619CFF"), pesu_rgbif_df$state)
leaflet(pesu_rgbif_df) %>%
addTiles() %>%
addCircleMarkers(pesu_rgbif_df$x,
pesu_rgbif_df$y,
popup = pesu_rgbif_df$species,
weight = 2,
color = colors(pesu_rgbif_df$state),
fillOpacity = 0.7) %>%
addMiniMap(position = 'topright',
width = 100,
height = 100,
toggleDisplay = FALSE) %>%
addScaleBar(position = "bottomright")3 Dryad Data
This data set was comparing the importance of temperature, fat mass, and humidity for hibernation energy expenditure.
Utilmately they found that temperature is not always the most important factor driving energy expenditure.
main_map <- ggplot(hiber, aes(Longitude, Latitude)) +
geom_polygon(data = state, aes(x=long, y = lat, group = group),
fill = "gray", color="white") +
geom_polygon(data = tn, aes(x=long, y = lat, group = group),
fill = "white", color="black") +
geom_polygon(data = ky, aes(x=long, y = lat, group = group),
fill = "white", color="black") +
geom_point(aes(color = Temperature, size = Sex)) +
coord_fixed(xlim = c(-87.8,-87.4), ylim = c(36.4,36.75)) +
xlab("Longitude") + ylab("Latitude") + ggtitle("Understanding Hibernation") +
guides(color=guide_legend("Temperature", override.aes = list(size = 3))) +
guides(size=guide_legend("Sex")) +
theme_bw() + theme(plot.title = element_text(hjust = 0.5)) +
theme(legend.position = "right") +
theme(legend.title.align = 0.5, legend.box.just = "center") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "lightblue"))
main_map
3.1 Inset Map
inset <- ggplot() +
geom_polygon(data = state, aes(x=long, y = lat, group = group),
fill = "gray", color="white") +
geom_polygon(data = tn, aes(x=long, y = lat, group = group),
fill = "lightblue", color="black") +
geom_polygon(data = ky, aes(x=long, y = lat, group = group),
fill = "lightblue", color="black") +
geom_polygon(data = montco, aes(x=long, y = lat, group = group),
fill = "blue", color="black") +
geom_polygon(data = stewco, aes(x=long, y = lat, group = group),
fill = "blue", color="black") +
geom_polygon(data = triggco, aes(x=long, y = lat, group = group),
fill = "blue", color="black") +
geom_polygon(data = chrisco, aes(x=long, y = lat, group = group),
fill = "blue", color="black") +
coord_map(xlim = c(-90,-82), ylim = c(39,35), "polyconic") +
theme(panel.background = element_rect(fill = "pink"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
axis.line=element_blank(), axis.text.x=element_blank(), axis.text.y=element_blank(),axis.ticks=element_blank(),
axis.title.x=element_blank(), axis.title.y=element_blank()) +
theme(plot.margin=grid::unit(c(0,0,0,0), "mm"))
inset
ggdraw() +
draw_plot(main_map) +
draw_plot(inset, x = 0.16, y = 0.11, width = 0.25, height = 0.25)
4 Probability
bioclim <- getData(name = "worldclim", res = 2.5, var = "bio", path = "./")
names(bioclim) <- c("Ann Mean Temp","Mean Diurnal Range","Isothermality","Temperature Seasonality",
"Max Temp Warmest Mo","Min Temp Coldest Mo","Ann Temp Range","Mean Temp Wettest Qtr",
"Mean Temp Driest Qtr","Mean Temp Warmest Qtr","Mean Temp Coldest Qtr","Annual Precip",
"Precip Wettest Mo","Precip Driest Mo","Precip Seasonality","Precip Wettest Qtr",
"Precip Driest Qtr","Precip Warmest Qtr","Precip Coldest Qtr")
bio_extent <- extent(x = c(
min(pesu_rgbif_df$x),
max(pesu_rgbif_df$x),
min(pesu_rgbif_df$y),
max(pesu_rgbif_df$y)))
bioclim_extent <- crop(x = bioclim, y = bio_extent)
bioclim_model <- bioclim(x = bioclim_extent, p = cbind(pesu_rgbif_df$x,pesu_rgbif_df$y))
presence_model <- dismo::predict(object = bioclim_model,
x = bioclim_extent,
ext = bio_extent)gplot(presence_model) +
geom_polygon(data = us, aes(x= long, y = lat, group = group),
fill = "gray", color="black") +
geom_raster(aes(fill=value)) +
geom_polygon(data = us, aes(x= long, y = lat, group = group),
fill = NA, color="black") +
geom_point(data = pesu_rgbif_df, aes(x = x, y = y), size = 2, color = "black", alpha = 0.5) +
scale_fill_gradientn(colours=c("brown","yellow","darkgreen"), "Probability") +
coord_fixed(xlim = c(-95,-75), ylim = c(25,41)) +
xlab("Longitude") + ylab("Latitude") + ggtitle("Probability of PESU Occurrence") +
theme_bw() + theme(plot.title = element_text(hjust = 0.5)) + theme(legend.position = "right") +
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "lightblue"))
colors <- c("brown","yellow","darkgreen")
leaflet() %>%
addTiles() %>%
addRasterImage(presence_model, colors = colors, opacity = 0.8) %>%
addCircleMarkers(pesu_rgbif_df$x,
pesu_rgbif_df$y,
weight = 1,
color = "grey",
fillColor = "green",
fillOpacity = 0.7) %>%
addMiniMap(position = 'topright',
width = 100,
height = 100,
toggleDisplay = FALSE) %>%
addScaleBar(position = "bottomright")